CN116704733B - Aging early warning method and system for aluminum alloy cable - Google Patents
Aging early warning method and system for aluminum alloy cable Download PDFInfo
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Abstract
The application provides an aging early warning method and system for an aluminum alloy cable, which relate to the technical field of cable early warning and comprise the following steps: the method comprises the steps of collecting modal data of a first aluminum alloy cable to obtain a first modeling data set, modeling to output a first cable simulation model, carrying out state test to output a first prediction aging index for identifying abnormal operation of the cable, carrying out adaptability decomposition of a cable aging neighborhood unit to obtain a first prediction aging area and a first prediction aging depth, obtaining a real-time monitoring image set, inputting a first adaptability function and a second adaptability function, outputting the first adaptability index and the second adaptability index by using an IPSO algorithm, and generating first early warning information. The application solves the technical problems that in the prior art, the aging analysis of the aluminum alloy cable is only carried out on an aging center, so that the aging defect cannot be accurately predicted, the cable aging problem is processed with hysteresis, and the operation safety and stability of a cable system are poor.
Description
Technical Field
The application relates to the technical field of cable early warning, in particular to an aging early warning method and system for an aluminum alloy cable.
Background
The aluminum alloy cable is important equipment widely applied to the fields of power transmission, communication, construction and the like, and along with long-time operation of the cable system, ageing problems are gradually revealed, and the cable performance is possibly reduced, faults and even accidents are possibly caused. The aging pre-warning method of the aluminum alloy cable commonly used at present has certain defects, and certain lifting space exists for the aging pre-warning of the aluminum alloy cable.
Disclosure of Invention
The application provides an aging early warning method and system for an aluminum alloy cable, and aims to solve the technical problems that in the prior art, aging analysis of the aluminum alloy cable is only carried out on an aging center, so that aging defects cannot be accurately predicted, and further the cable aging problem is delayed in treatment, and the operation safety and stability of a cable system are poor.
In view of the problems, the application provides an aging early warning method and system for an aluminum alloy cable.
According to a first aspect of the present disclosure, an aging pre-warning method for an aluminum alloy cable is provided, the method comprising: acquiring modal data of a first aluminum alloy cable to obtain a first modeling data set; modeling the first modeling data set by connecting a simulation platform, and outputting a first cable simulation model; performing state test according to the first cable simulation model, and outputting a first predicted aging index for identifying abnormal operation of the cable; performing cable aging neighborhood unit fitness decomposition on the first predicted aging index to obtain a first predicted aging area and a first predicted aging depth; acquiring a real-time monitoring image set of the first aluminum alloy cable; inputting the real-time monitoring image set into a first fitness function and a second fitness function, and outputting a first fitness index and a second fitness index by using an IPSO algorithm, wherein the first fitness function is an aging area fitness function, and the second fitness function is an aging depth fitness function; and generating first early warning information according to the first fitness index and the second fitness index.
In another aspect of the disclosure, an aging warning system for an aluminum alloy cable is provided, the system comprising: the modal data acquisition module is used for acquiring modal data of the first aluminum alloy cable to obtain a first modeling data set; the modeling module is used for being connected with the simulation platform to model the first modeling data set and output a first cable simulation model; the state test module is used for carrying out state test according to the first cable simulation model and outputting a first prediction aging index for identifying abnormal operation of the cable; the fitness decomposition module is used for performing fitness decomposition on the cable aging neighborhood unit on the first predicted aging index to obtain a first predicted aging area and a first predicted aging depth; the monitoring image acquisition module is used for acquiring a real-time monitoring image set of the first aluminum alloy cable; the fitness index acquisition module is used for inputting the real-time monitoring image set into a first fitness function and a second fitness function, and outputting the first fitness index and the second fitness index by using an IPSO algorithm, wherein the first fitness function is an aging area fitness function, and the second fitness function is an aging depth fitness function; the early warning information generation module is used for generating first early warning information according to the first fitness index and the second fitness index.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
the method comprises the steps of collecting modal data of a first aluminum alloy cable to obtain a first modeling data set, modeling the first modeling data set, outputting a first cable simulation model, performing state test, outputting a first prediction aging index for identifying abnormal operation of the cable, performing adaptability decomposition of a cable aging neighborhood unit to obtain a first prediction aging area and a first prediction aging depth, obtaining a real-time monitoring image set, inputting a first adaptability function and a second adaptability function, outputting the first adaptability index and the second adaptability index by using an IPSO algorithm, and generating first early warning information according to the first adaptability index and the second adaptability index. The technical problems that ageing analysis of an aluminum alloy cable in the prior art is only carried out aiming at an ageing center, so that ageing defects cannot be accurately predicted, and then the cable ageing problem is processed with hysteresis, and the operation safety and stability of a cable system are poor are solved.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
FIG. 1 is a schematic flow chart of an aging early warning method for an aluminum alloy cable according to an embodiment of the application;
fig. 2 is a schematic flow chart of outputting a first predicted aging area and a first predicted aging depth in an aging early warning method for an aluminum alloy cable according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a possible process of generating first warning information in the aging warning method of an aluminum alloy cable according to an embodiment of the present application;
fig. 4 is a schematic diagram of a possible structure of an aging warning system for an aluminum alloy cable according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a modal data acquisition module 10, a modeling module 20, a state test module 30, an fitness decomposition module 40, a monitoring image acquisition module 50, a fitness index acquisition module 60 and an early warning information generation module 70.
Detailed Description
According to the embodiment of the application, by providing the aging early warning method for the aluminum alloy cable, the technical problems that the aging defect cannot be accurately predicted due to the fact that the aging analysis of the aluminum alloy cable is only performed for an aging center in the prior art, and further the cable aging problem is delayed in processing, and the operation safety and stability of a cable system are poor are solved, and the neighborhood aging defect characteristics of the cable are accurately identified by identifying the neighborhood aging degree around the center aging degree, so that the neighborhood change is found, the aging of the cable is predicted, the early discovery and processing of the cable aging problem are realized, and the safe and stable operation of the cable system is ensured.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, an embodiment of the present application provides an aging early warning method for an aluminum alloy cable, where the method includes:
step S100: acquiring modal data of a first aluminum alloy cable to obtain a first modeling data set;
in particular, the first aluminum alloy cable refers to an aluminum alloy cable of the health condition to be evaluated, possibly as part or whole of a power grid system, and information of the first aluminum alloy cable is collected, including physical properties of the cable, including dimensions, shapes, conductor materials, etc., operating conditions, including temperature, humidity, load flow, etc., and historical aging conditions, which are used to create a first modeled data set of the cable.
Step S200: modeling the first modeling data set by connecting a simulation platform, and outputting a first cable simulation model;
in particular, the simulation platform is a software tool for analyzing and simulating the performance of various systems, in the course of which a first modeling data set is input into the simulation platform in order to simulate the ageing behaviour of the aluminium alloy cable. Modeling is a process of describing system behaviors by using mathematical and physical principles, and a simulation model is created through information of a first modeling data set to simulate performances of the aluminum alloy cable under different working conditions, such as temperature, humidity, load flow and the like. These models may be based on finite element methods, statistical models, or other numerical methods, depending on the analysis requirements and available data.
Based on the first modeling data set and the selected modeling method, the simulation platform generates and outputs a first cable simulation model that can be used to predict the aging of the cable under a given operating condition, e.g., the aging rate of the cable, the points of failure that may occur, and key factors that may cause performance degradation under different conditions. Through the process, the deep knowledge of the aging behavior of the aluminum alloy cable under different working conditions can be obtained, so that the evaluation of the health condition of the cable is facilitated, and an effective early warning strategy is formulated.
Step S300: performing state test according to the first cable simulation model, and outputting a first predicted aging index for identifying abnormal operation of the cable;
specifically, the state test is to evaluate the performance of the aluminum alloy cable under the actual working condition based on the first cable simulation model, in the process, the performance of the cable under different conditions, such as temperature, humidity, load flow and the like, can be simulated and tested, and potential abnormality or problem can be detected by analyzing the simulation result.
According to the result of the state test, a first predicted aging index for representing abnormal operation of the cable is determined and used as output, and the index comprises parameters such as damage depth, aging rate, damage area and the like and is used for identifying problems of the cable in the normal operation process, so that timely intervention and maintenance are performed. Through the process, the performance of the cable under the actual working condition can be evaluated, so that potential problems can be found, and a basis is provided for subsequent early warning and maintenance.
Step S400: performing cable aging neighborhood unit fitness decomposition on the first predicted aging index to obtain a first predicted aging area and a first predicted aging depth;
specifically, the neighborhood of cells is a set of areas around the cable that may include areas adjacent to a central aging level, such as a higher or lower aging level, and the aging behavior of the cable may be better understood and predicted by analyzing the changes in the neighborhood of cells.
Fitness decomposition is a multi-objective optimization method for decomposing a complex problem into multiple sub-problems for ease of solution. Here, the fitness decomposition is used to decompose the first predicted aging index into two indexes, i.e., a first predicted aging area and a first predicted aging depth, so that the aging condition of the aluminum alloy cable can be more specifically quantified.
The first predicted aging area represents the total area of the damaged area of the cable, and a larger aging area may mean that the damage degree of the cable is more serious, and urgent measures need to be taken for maintenance or replacement; the first predicted aging depth represents the depth of the damaged area of the cable, a deeper aging depth meaning that damage to the cable may affect its performance, requiring special attention and timely treatment.
Further, as shown in fig. 2, step S400 of the present application further includes:
step S410: acquiring data according to the first cable simulation model to obtain a test sample image, wherein the test sample image comprises a historical aging image set based on the first aluminum alloy cable;
step S420: performing defect positioning on the test sample image, and obtaining neighborhood aging defect characteristics according to a defect positioning result;
step S430: and carrying out cable aging neighborhood unit fitness decomposition on the first prediction aging index based on the neighborhood aging defect characteristics, and outputting a first prediction aging area and a first prediction aging depth.
Specifically, the first cable simulation model is utilized to collect data of the aluminum alloy cable, wherein the data comprise performances, damage characteristics and other information related to aging of the cable under different working conditions. The test sample images are representative image data extracted from the simulation model, including a set of historical aging images based on the first aluminum alloy cable, which images can be used to train and validate the machine learning model, further analyzing and predicting aging behavior of the cable.
Defect localization is a process of identifying and locating aged defects present in a test sample image, such as cracks, corrosion, oxidation, etc., specifically, denoising, enhancing contrast, adjusting brightness, etc., of an original image to improve image quality, detecting edges and extracting contours using an image processing algorithm, such as a Canny edge detection algorithm (a multi-step algorithm for detecting edges of any input image, mainly 5 steps, in order of gaussian filtering, pixel gradient calculation, non-maximum suppression, hysteresis thresholding, and isolated weak edge suppression), highlighting defective areas. The images are binarized, then morphological operations such as corrosion, expansion and the like are used for further highlighting defect characteristics, and target detection and identification are carried out on the test sample images and defect areas are positioned by means of machine learning or deep learning technologies such as convolutional neural networks.
And obtaining neighborhood aging defect characteristics according to the defect positioning result, wherein the characteristics comprise information such as shape, size, depth, distribution and the like of the defects, and the health condition of the cable can be better known and a basis is provided for subsequent early warning and maintenance by analyzing the characteristics.
And decomposing a first predicted aging index based on the neighborhood aging defect characteristics, so as to decompose a complex aging problem into two more specific sub-problems, namely an aging area and an aging depth, wherein the first predicted aging area represents the total area of a cable damage area, the first predicted aging depth represents the depth of the cable damage area, and the two indexes are beneficial to more specifically knowing the health condition of the cable and provide basis for subsequent early warning and maintenance.
Further, step S420 of the present application further includes:
step S421: dividing edge coordinate points of the neighborhood aging defect characteristics according to defect positioning results, and outputting a defect edge coordinate set;
step S422: performing neighborhood region configuration on the first prediction aging index, and outputting a first neighborhood positioning region, wherein the first neighborhood positioning region is the maximum distance of expansion of the defect edge coordinate set;
step S423: and carrying out defect feature identification according to the first neighborhood positioning interval to obtain neighborhood aging defect features.
Specifically, the edge coordinate points of the defect region are divided, which means that the boundary of the defect region is extracted from the defect localization result and converted into a series of edge coordinate points, which can help describe the shape and range of the defect more clearly for further analysis and processing. The extracted edge coordinate points are arranged into a coordinate set to be used as output, and a defect edge coordinate set is obtained, wherein the defect edge coordinate set contains boundary information of all defect areas and can be used for further analyzing the health condition of the cable or providing basis for taking maintenance measures.
When the neighborhood region is configured, an appropriate neighborhood range is determined according to the first predicted aging index, wherein the range comprises the area around the defect and is used for helping to further analyze the aging behavior of the cable and the influence on the surrounding area. The defect edge coordinate set is extended to determine a maximum distance that identifies a first neighborhood positioning interval, i.e., a region range around the defect. The maximum distance of expansion of the defect edge coordinate set is determined through a geometric calculation technology, wherein the maximum distance comprises the distance between calculation points, the circumscribed rectangle of the defect boundary and the like, and the neighborhood is analyzed based on the first predicted aging index and the defect edge coordinate set, so that the influence of the aging behavior on the surrounding area is better clarified.
The image data in the first neighborhood positioning zone is subjected to defect feature recognition, so that the neighborhood aging defect features are detected and recognized in the surrounding range of the defects, and the neighborhood aging defect features are recognized and classified by using a feature extraction algorithm, such as SIFT (Scale-invariant feature transform, scale invariant feature transform, an algorithm for detecting and describing local features in images and machine vision, searching extremum points in a spatial Scale, extracting positions, scales and rotation invariant) in the first neighborhood positioning zone.
After feature identification, the obtained neighborhood aging defect features may include defect shape, size, depth, distribution, density and other information, and the features are helpful for evaluating the influence of cable aging behavior on surrounding areas and providing basis for subsequent early warning and maintenance.
Step S500: acquiring a real-time monitoring image set of the first aluminum alloy cable;
specifically, the aluminum alloy cable is continuously tracked and monitored in real time by adopting proper sensors and imaging technology, for example, a thermal infrared imager is adopted to detect the change of the surface temperature of the cable, so that the running state and the aging condition of the cable are known; optical imaging techniques, such as high resolution cameras, are used to capture details and anomalies on the cable surface. A series of image data about the aluminum alloy cable are acquired, the images can reflect aging conditions of the cable under actual working conditions, such as damage areas, cracks, oxidization and the like, and the health condition of the cable can be more intuitively known by analyzing the images. Through the step, the performance of the cable under the actual working condition is clarified, and a basis is provided for subsequent early warning and maintenance.
Step S600: inputting the real-time monitoring image set into a first fitness function and a second fitness function, and outputting a first fitness index and a second fitness index by using an IPSO algorithm, wherein the first fitness function is an aging area fitness function, and the second fitness function is an aging depth fitness function;
specifically, the fitness function is a method for evaluating and comparing the merits of a certain solution, in this example, the first fitness function is an aging area fitness function, which is used for evaluating the total area of a cable damaged area, specifically, calculating the absolute value of the difference between the aging areas of coordinate points at two sides of the cable aging unit, calculating the absolute value of the difference between the aging areas obtained by predicting the coordinate points at two sides of the cable aging unit, and calculating the sum of two absolute values, namely, the first fitness function; the second fitness function is an aging depth fitness function and is used for evaluating the depth of a cable damaged area, specifically, calculating the absolute value of the difference of the damaged depths of coordinate points on two sides of the cable aging unit, calculating the absolute value of the difference of the damaged depths obtained by prediction of the coordinate points on two sides of the cable aging unit, and calculating the sum of the two absolute values to obtain the second fitness function.
Wherein the first fitness function is as follows:
wherein F (y) 1 ) Is a first fitness function of the damaged part of the cable; salpha m-1 、Sα m The aging areas of coordinate points on two sides of the cable aging unit are respectively;the aging area is obtained by predicting coordinate points on two sides of the cable aging unit.
The second fitness function is as follows:
wherein F (y) 2 ) Is a second fitness function of the damaged part of the cable; lbeta m 、Lβ m+1 The damage depth of coordinate points on two sides of the cable ageing unit is respectively,the damage depth obtained by predicting coordinate points on two sides of the cable aging unit is obtained.
And inputting the real-time monitoring image set into the two fitness functions, and evaluating and comparing the health condition of the cable according to the images collected under the actual working conditions, so as to further determine the aging condition of the cable.
The IPSO (improved particle swarm optimization) algorithm is an intelligent optimization method for solving a multi-objective optimization problem, and can improve the convergence speed and the resolution quality of an optimization process.
Step S700: and generating first early warning information according to the first fitness index and the second fitness index.
Further, as shown in fig. 3, step S700 of the present application further includes:
step S710: weight calculation is carried out according to the first fitness index and the second fitness index, and a neighborhood fitness index is obtained;
step S720: when the neighborhood fitness index is larger than a preset neighborhood fitness index, comparing the first fitness index with the second fitness index to obtain a first comparison result;
step S730: and generating first early warning information according to the first comparison result.
Specifically, the relative importance of each in the neighborhood fitness index is determined according to the first fitness index and the second fitness index, the weight is determined through methods such as analysis of historical data, industry experience or expert opinion, and reasonable consideration of the two fitness indexes in the process of calculating the neighborhood fitness index is ensured.
And based on the weight calculation results of the first fitness index and the second fitness index, carrying out weighted average on the first fitness index and the second fitness index to obtain a comprehensive index, namely a neighborhood fitness index, which can more comprehensively reflect the health condition of the aluminum alloy cable and provide basis for subsequent early warning and maintenance.
The preset neighborhood fitness index is a reference value, and is determined by industry standards, enterprise experience or other relevant bases, the index is used for judging whether the current cable health condition reaches the degree of taking measures, when the neighborhood fitness index is larger than the preset neighborhood fitness index, a numerical comparison method is adopted to compare the numerical values of the first fitness index (aging area) and the second fitness index (aging depth) so as to determine which factor leads the cable health condition to exceed a preset range or judge which factor is more serious, and information is provided for subsequent early warning and maintenance decisions.
And generating corresponding early warning information according to the first comparison result, wherein the corresponding early warning information is automatically matched and generated according to the first comparison result by utilizing a preset early warning rule base, and the rule base can be continuously optimized and perfected according to actual requirements and experience.
The first early warning information comprises an abnormal type, an early warning level, an early warning position, recommended measures and the like, wherein the abnormal type is a cause which clearly causes the health condition of the cable to exceed a preset range, namely whether the aging area is overlarge or the aging depth is overlarge; the early warning level is a level determined according to the first comparison result, such as slight, medium, serious and the like, so as to reflect the urgency of the actual problem; the early warning position is a specific position describing that problems exist in the first neighborhood positioning interval, so that the follow-up investigation and processing are facilitated; the recommended measures are to provide corresponding recommended measures such as inspection, maintenance, replacement and the like according to the abnormality type and the early warning level.
Further, step S700 of the present application further includes:
step S740: load flow information of the first aluminum alloy cable is obtained;
step S750: performing aging rate identification according to the load flow information, and outputting a first attenuation rate;
step S760: and updating and identifying the neighborhood fitness index according to the first attenuation rate, and outputting second early warning information.
Specifically, the load flow information refers to relevant parameters such as current or power born by the cable in the actual running process, and the information can reflect the actual load situation born by the cable and is used for evaluating the health condition of the cable and analyzing the aging reason of the cable. The load flow information of the first aluminum alloy cable is obtained by adopting methods such as monitoring equipment, SCADA (supervisory control and data acquisition) systems or field inspection, and the like, and load flow data of the cable are collected in real time or periodically by using monitoring equipment such as a current transformer, an electric power monitoring instrument and the like; automatically collecting the operation state and load flow information of the cable through a monitoring and data acquisition System (SCADA); the cable is inspected regularly by field staff and the load flow information is recorded.
The aging rate of the aluminum alloy cable is evaluated according to the load flow information, and a statistical model is established to identify the aging rate, namely the aging rate of the cable performance along with time, which is closely related to the service life, the health condition and the possible fault risk of the cable by analyzing the relation between the historical load flow data and the cable performance attenuation. After the aging rate is identified, the result is a first attenuation rate, and the index can be used for evaluating the aging degree and the potential risk of the cable under the current load flow.
Updating and identifying the neighborhood fitness index according to the first attenuation rate by adopting methods such as data fusion, dynamic threshold adjustment, expert system and the like, and fusing the data of the neighborhood fitness index with the first attenuation rate to update the identification result; dynamically adjusting a threshold value of a preset neighborhood fitness index according to the first attenuation rate so as to improve the accuracy of early warning information; by means of an expert system, knowledge and experience of a field expert are integrated into the early warning information generation process, and accuracy and reliability of early warning are improved.
Through the updating and identifying of the neighborhood fitness index, the neighborhood fitness index is evaluated more accurately by utilizing the latest aging rate, so that the health condition of the aluminum alloy cable is monitored better, and corresponding second early warning information is generated after updating and identifying, wherein the second early warning information comprises early warning types, early warning levels, early warning positions, suggested measures and the like.
In summary, the aging early warning method and system for the aluminum alloy cable provided by the embodiment of the application have the following technical effects:
the method comprises the steps of collecting modal data of a first aluminum alloy cable to obtain a first modeling data set, modeling the first modeling data set, outputting a first cable simulation model, performing state test, outputting a first prediction aging index for identifying abnormal operation of the cable, performing adaptability decomposition of a cable aging neighborhood unit to obtain a first prediction aging area and a first prediction aging depth, obtaining a real-time monitoring image set, inputting a first adaptability function and a second adaptability function, outputting the first adaptability index and the second adaptability index by using an IPSO algorithm, and generating first early warning information according to the first adaptability index and the second adaptability index.
The technical problems that ageing analysis of an aluminum alloy cable in the prior art is only carried out aiming at an ageing center, so that ageing defects cannot be accurately predicted, and then the cable ageing problem is processed with hysteresis, and the operation safety and stability of a cable system are poor are solved.
Example two
Based on the same inventive concept as the aging pre-warning method of the aluminum alloy cable in the foregoing embodiment, as shown in fig. 4, the present application provides an aging pre-warning system of an aluminum alloy cable, the system comprising:
the modal data acquisition module 10 is used for acquiring modal data of the first aluminum alloy cable to obtain a first modeling data set;
the modeling module 20 is used for being connected with a simulation platform to model the first modeling data set and output a first cable simulation model;
the state test module 30 is configured to perform a state test according to the first cable simulation model, and output a first predicted aging index that identifies abnormal operation of the cable;
the fitness decomposition module 40 is configured to perform fitness decomposition on the cable aging neighborhood unit on the first predicted aging index, so as to obtain a first predicted aging area and a first predicted aging depth;
a monitoring image acquisition module 50, wherein the monitoring image acquisition module 50 is used for acquiring a real-time monitoring image set of the first aluminum alloy cable;
the fitness index obtaining module 60 is configured to input the real-time monitoring image set into a first fitness function and a second fitness function, and output the first fitness index and the second fitness index by using an IPSO algorithm, where the first fitness function is an aging area fitness function, and the second fitness function is an aging depth fitness function;
the early warning information generation module 70 is configured to generate first early warning information according to the first fitness index and the second fitness index by the early warning information generation module 70.
Further, the system further comprises:
the data acquisition module is used for acquiring data according to the first cable simulation model to obtain a test sample image, wherein the test sample image comprises a historical aging image set based on the first aluminum alloy cable;
the defect positioning module is used for performing defect positioning on the test sample image and obtaining neighborhood aging defect characteristics according to a defect positioning result;
and the fitness decomposition module is used for carrying out fitness decomposition on the cable aging neighborhood unit on the first prediction aging index based on the neighborhood aging defect characteristics and outputting a first prediction aging area and a first prediction aging depth.
Further, the system further comprises:
the edge coordinate point dividing module is used for dividing edge coordinate points of the neighborhood aging defect characteristics according to the defect positioning result and outputting a defect edge coordinate set;
the neighborhood region configuration module is used for carrying out neighborhood region configuration on the first prediction aging index and outputting a first neighborhood positioning region, wherein the first neighborhood positioning region is the maximum distance of the expansion of the defect edge coordinate set;
and the defect characteristic recognition module is used for carrying out defect characteristic recognition according to the first neighborhood positioning interval to obtain neighborhood aging defect characteristics.
Further, the first fitness function is as follows:
wherein F (y) 1 ) Is a first fitness function of the damaged part of the cable; salpha m-1 、Sα m The aging areas of coordinate points on two sides of the cable aging unit are respectively;the aging area is obtained by predicting coordinate points on two sides of the cable aging unit.
Further, the second fitness function is as follows:
wherein F (y) 2 ) Is a second fitness function of the damaged part of the cable; lbeta m 、Lβ m+1 The damage depth of coordinate points on two sides of the cable ageing unit is respectively,the damage depth obtained by predicting coordinate points on two sides of the cable aging unit is obtained.
Further, the system further comprises:
the weight calculation module is used for carrying out weight calculation according to the first fitness index and the second fitness index to obtain a neighborhood fitness index;
the comparison module is used for comparing the first fitness index with the second fitness index when the neighborhood fitness index is larger than a preset neighborhood fitness index to obtain a first comparison result;
and the first early warning information generation module is used for generating first early warning information according to the first comparison result.
Further, the system further comprises:
the load flow information acquisition module is used for acquiring load flow information of the first aluminum alloy cable;
the aging rate identification module is used for carrying out aging rate identification according to the load flow information and outputting a first attenuation rate;
and the updating and identifying module is used for updating and identifying the neighborhood fitness index according to the first attenuation rate and outputting second early warning information.
The foregoing detailed description of the method for early warning aging of an aluminum alloy cable can clearly be known to those skilled in the art, and the device disclosed in the embodiment corresponds to the method disclosed in the embodiment, so that the description is simpler, and the relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (4)
1. The aging early warning method for the aluminum alloy cable is characterized by comprising the following steps of:
acquiring modal data of a first aluminum alloy cable to obtain a first modeling data set;
modeling the first modeling data set by connecting a simulation platform, and outputting a first cable simulation model;
performing state test according to the first cable simulation model, and outputting a first predicted aging index for identifying abnormal operation of the cable;
performing cable aging neighborhood unit fitness decomposition on the first predicted aging index to obtain a first predicted aging area and a first predicted aging depth;
acquiring a real-time monitoring image set of the first aluminum alloy cable;
inputting the real-time monitoring image set into a first fitness function and a second fitness function, and outputting a first fitness index and a second fitness index by using an IPSO algorithm, wherein the first fitness function is an aging area fitness function, and the second fitness function is an aging depth fitness function;
generating first early warning information according to the first fitness index and the second fitness index;
the cable aging neighborhood unit fitness decomposition is carried out on the first prediction aging index, and the method comprises the following steps:
acquiring data according to the first cable simulation model to obtain a test sample image, wherein the test sample image comprises a historical aging image set based on the first aluminum alloy cable;
performing defect positioning on the test sample image, and obtaining neighborhood aging defect characteristics according to a defect positioning result;
performing cable aging neighborhood unit fitness decomposition on the first prediction aging index based on the neighborhood aging defect characteristics, and outputting a first prediction aging area and a first prediction aging depth;
obtaining neighborhood aging defect characteristics according to defect positioning results, wherein the method comprises the following steps:
dividing edge coordinate points of the neighborhood aging defect characteristics according to defect positioning results, and outputting a defect edge coordinate set;
performing neighborhood region configuration on the first prediction aging index, and outputting a first neighborhood positioning region, wherein the first neighborhood positioning region is the maximum distance of expansion of the defect edge coordinate set;
performing defect feature identification according to the first neighborhood positioning interval to obtain neighborhood aging defect features;
the first fitness function is as follows:
wherein F (y) 1 ) Is a first fitness function of the damaged part of the cable; salpha m-1 、Sα m The aging areas of coordinate points on two sides of the cable aging unit are respectively;the aging area is obtained by predicting coordinate points on two sides of the cable aging unit;
the second fitness function is as follows:
wherein F (y) 2 ) Is a second fitness function of the damaged part of the cable; lbeta m 、Lβ m+1 The damage depth of coordinate points on two sides of the cable ageing unit is respectively,the damage depth obtained by predicting coordinate points on two sides of the cable aging unit is obtained.
2. The method of claim 1, wherein the method further comprises:
weight calculation is carried out according to the first fitness index and the second fitness index, and a neighborhood fitness index is obtained;
when the neighborhood fitness index is larger than a preset neighborhood fitness index, comparing the first fitness index with the second fitness index to obtain a first comparison result;
and generating first early warning information according to the first comparison result.
3. The method of claim 2, wherein the method further comprises:
load flow information of the first aluminum alloy cable is obtained;
performing aging rate identification according to the load flow information, and outputting a first attenuation rate;
and updating and identifying the neighborhood fitness index according to the first attenuation rate, and outputting second early warning information.
4. An aging pre-warning system for an aluminum alloy cable, which is characterized by being used for implementing the aging pre-warning method for an aluminum alloy cable according to any one of claims 1 to 3, comprising:
the modal data acquisition module is used for acquiring modal data of the first aluminum alloy cable to obtain a first modeling data set;
the modeling module is used for being connected with the simulation platform to model the first modeling data set and output a first cable simulation model;
the state test module is used for carrying out state test according to the first cable simulation model and outputting a first prediction aging index for identifying abnormal operation of the cable;
the fitness decomposition module is used for performing fitness decomposition on the cable aging neighborhood unit on the first predicted aging index to obtain a first predicted aging area and a first predicted aging depth;
the monitoring image acquisition module is used for acquiring a real-time monitoring image set of the first aluminum alloy cable;
the fitness index acquisition module is used for inputting the real-time monitoring image set into a first fitness function and a second fitness function, and outputting the first fitness index and the second fitness index by using an IPSO algorithm, wherein the first fitness function is an aging area fitness function, and the second fitness function is an aging depth fitness function;
the early warning information generation module is used for generating first early warning information according to the first fitness index and the second fitness index;
the data acquisition module is used for acquiring data according to the first cable simulation model to obtain a test sample image, wherein the test sample image comprises a historical aging image set based on the first aluminum alloy cable;
the defect positioning module is used for performing defect positioning on the test sample image and obtaining neighborhood aging defect characteristics according to a defect positioning result;
the fitness decomposition module is used for performing fitness decomposition on the cable aging neighborhood unit on the first prediction aging index based on the neighborhood aging defect characteristics and outputting a first prediction aging area and a first prediction aging depth;
the edge coordinate point dividing module is used for dividing edge coordinate points of the neighborhood aging defect characteristics according to the defect positioning result and outputting a defect edge coordinate set;
the neighborhood region configuration module is used for carrying out neighborhood region configuration on the first prediction aging index and outputting a first neighborhood positioning region, wherein the first neighborhood positioning region is the maximum distance of the expansion of the defect edge coordinate set;
the defect feature recognition module is used for carrying out defect feature recognition according to the first neighborhood positioning interval to obtain neighborhood aging defect features;
the first fitness function is as follows:
wherein F (y) 1 ) Is a first fitness function of the damaged part of the cable; salpha m-1 、Sα m The aging areas of coordinate points on two sides of the cable aging unit are respectively;the aging area is obtained by predicting coordinate points on two sides of the cable aging unit;
the second fitness function is as follows:
wherein F (y) 2 ) Is a second fitness function of the damaged part of the cable; lbeta m 、Lβ m+1 The damage depth of coordinate points on two sides of the cable ageing unit is respectively,the damage depth obtained by predicting coordinate points on two sides of the cable aging unit is obtained.
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